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Related Concept Videos

Reducing Line Loss01:18

Reducing Line Loss

180
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
180
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
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Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

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The practical equivalent circuits of single-phase two-winding transformers exhibit significant deviations from their idealized versions due to the inherent properties of winding resistance and finite core permeability. These properties result in real and reactive power losses, affecting the transformer's performance. Understanding these deviations is crucial for designing more efficient transformers.
In a practical transformer, each winding exhibits resistance and leakage reactance. The...
483
The Ideal Transformer01:26

The Ideal Transformer

445
In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's...
445
Types Of Transformers01:16

Types Of Transformers

1.0K
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
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Instrument Transformers01:23

Instrument Transformers

114
Instrument transformers, comprising voltage transformers (VTs) and current transformers (CTs), play crucial roles in power substations by providing isolated replicas of current or voltage for measurement and protection purposes. Voltage transformers reduce the primary voltage to levels suitable for relay operation and measurement, while current transformers scale down the primary current. The primary winding of a current transformer often consists of a single turn, achieved by threading the...
114

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    This study introduces a large-scale underwater image dataset (LSUI) and a novel U-shape Transformer network for underwater image enhancement (UIE). The LSUI dataset and UIE network significantly improve image quality by addressing color channel and spatial attenuation.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Underwater imaging is degraded by light absorption and scattering from impurities.
    • Existing data-driven underwater image enhancement (UIE) methods lack large-scale datasets and struggle with inconsistent color channel and spatial attenuation.
    • Addressing these limitations is crucial for improving underwater image quality.

    Purpose of the Study:

    • To introduce a large-scale underwater image (LSUI) dataset with diverse scenes and high-fidelity reference images.
    • To develop a novel U-shape Transformer network for UIE, incorporating specialized modules for enhanced feature fusion and global modeling.
    • To improve contrast and saturation in underwater images by employing a novel loss function based on human visual principles.

    Main Methods:

    • Construction of the LSUI dataset, comprising 4279 real-world underwater image groups with paired clear references, semantic segmentation maps, and medium transmission maps.
    • Development of a U-shape Transformer network for UIE, featuring channel-wise multi-scale feature fusion transformer (CMSFFT) and spatial-wise global feature modeling transformer (SGFMT) modules.
    • Implementation of a novel loss function integrating RGB, LAB, and LCH color spaces to enhance contrast and saturation.

    Main Results:

    • The LSUI dataset provides a comprehensive resource for training and evaluating UIE algorithms.
    • The proposed U-shape Transformer network achieves state-of-the-art performance in underwater image enhancement.
    • Experimental validation demonstrates over 2dB superiority compared to existing methods on available datasets.

    Conclusions:

    • The developed LSUI dataset and U-shape Transformer network offer significant advancements in underwater image enhancement.
    • The specialized modules and loss function effectively address challenges related to color channel and spatial attenuation.
    • The findings pave the way for improved underwater image analysis and applications.